Episode 4: A Post-Data World – LLMs and the End of Data Paralysis

Prof. Garry Nolan is the Rachford and Carlota A. Harris Professor in the Department of Pathology at Stanford University School of Medicine. Garry Nolan is the Rachford and Carlota A. Harris Professor in the Department of Pathology at Stanford University School of Medicine. He has published over 330 research articles and is the holder of 50 US patents and has been honored as one of the top 25 inventors at Stanford University. His areas of research include hematopoiesis, cancer and leukemia, autoimmunity and inflammation, and computational approaches for network and systems immunology. Prof. Nolan’s efforts are to enable a deeper understanding not only of normal immune function, trauma, pathogen infection, and other inflammatory events but also detailed substructures of leukemias and solid cancers and their interactions with the immune system—which will enable wholly new understandings that will enable better management of disease and clinical outcomes. Talk title: A Post-Data World - LLMs And The End of Data Paralysis As the volume and complexity of single-cell and spatial biology data continue to grow, researchers face increasing challenges in extracting meaningful insights. In this forward-looking talk, Professor Gary Nolan explores how large language models (LLMs) and AI-driven tools are revolutionizing the interpretation of high-dimensional biological data. He will discuss how these technologies are enabling a shift from data paralysis to actionable understanding, unlocking new possibilities in disease research and precision medicine. Key learning objectives: • Understand the limitations of current approaches to single-cell and spatial biology data analysis and the concept of 'data paralysis'. • Explore how large language models (LLMs) and AI-driven tools are transforming the interpretation of high-dimensional biological data. • Learn how these emerging technologies can accelerate discovery and innovation in disease research and precision medicine.